Announcements

Colloquium on Digital Transformation Science

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  September 1630, 3 pm CT Causal Tensor Estimation Challenges & Opportunities in Cloud Operations Research Ishai Menache, Senior Principal Research Manager, Microsoft ResearchDevavrat Shah, Professor of Electrical Engineering and Computer Science, Massachusetts Institute of Technology REGISTER FOR ZOOM WEBINAR Cloud computing is a multi-billion-dollar business that has revolutionized the way computing resources are consumed. The emergence of cloud computing is attributed to lowering the risks for end-users (e.g., scaling-out resource usage based on demand), while allowing providers to reduce costs by managing compute resources at scale. In this talk, we present a framework for causal inference for the “panel” or “longitudinal” setting from the lens of tensor estimation. Traditionally, such panel or longitudinal settings are considered in econometrics literature for program or policy evaluation. Tensor will provide an overview of some of the challenges faced by providers at different dimensions of cloud operations. We will highlight the role of operations research and algorithms in increasing efficiency and return on investment of cloud infrastructure. As a concrete example, we zoom in on the Virtual Machine (VM) allocation problem, one of the fundamental problems in this area. VMs need to be assigned to physical machines in a way that reduces fragmentation and efficiently utilizes available machines. Motivated by advances in machine learning that provide good estimates of workload characteristics, we consider the effect of having extra information in the form of VM lifetimes and future demand. We show that even basic information about demand (e.g., its average) leads to algorithms with significantly better guarantees. Ishai Menache received his PhD in Electrical Engineering from the Technion, Israel Institute of Technology. He was a Postdoctoral Associate at the Laboratory for Information and Decision Systems (LIDS) at MIT. Ishai has been with Microsoft Research since 2011, where he is the founder and manager of the Cloud Operations Research (CORE) group. His research focuses on developing large-scale optimization frameworks for cloud systems and applications. More broadly, his areas of interest include systems and networking, optimization, and machine learning estimation has been considered in machine learning, where tantalizing statistical and computational tradeoffs have emerged for random observation models. We introduce a causal variant of tensor estimation that provides a unified view for prior works in econometrics and provides newer avenues to explore. We discuss a method for estimating such a causal variant of the tensor and various exciting directions for future research, including offline reinforcement learning. This is based on joint work with Alberto Abadie (MIT), Anish Agarwal (MIT), and Dennis Shen (MIT/UC Berkeley).Devavrat Shah is a professor in the Department of Electrical Engineering and Computer Science at MIT. His current research interests are at the interface of statistical inference and social data processing. His work has been recognized through prize paper awards in machine learning, operations research, and computer science, as well as career prizes including the 2010 Erlang prize from the INFORMS Applied Probability Society and the 2008 ACM Sigmetrics Rising Star Award. He is a distinguished alumnus of his alma mater IIT Bombay. He co-founded Celect, Inc. (now part of Nike) in 2013 to help retailers decide what to put where by accurately predicting demand using omni-channel data.

  
  

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